A Machine-Learning-Based Approach for Autonomous IoT Security
Abstrak
Machine learning techniques are proven valuable for the Internet of things (IoT) due to intelligent and cost-effective computing processes. In recent decades, wireless sensor network (WSN) and machine learning are integrated to give significant improvements for energy-based systems. However, resourceful routes analytic with nominal energy consumption are some demanding challenges. Moreover, WSN operates in an unpredictable space and a lot of network threats can be harmful to smart and secure data gathering. Consequently, security against such threats is another major concern for low-power sensors. Therefore, we aim to present a machine learning-based approach for autonomous IoT Security to achieve optimal energy efficiency and reliable transmissions. First, the proposed protocol optimizes network performance using a model-free Q-learning algorithm and achieves fault-tolerant data transmission. Second, it accomplishes data confidentiality against adversaries using a cryptography-based deterministic algorithm. The proposed protocol demonstrates better conclusions than other existing solutions.
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Raniyah Wazirali Tarik Abu-Ain Rami Ahmad
23 Juni 2022
Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.
Tariq Shahzad Umair Ahmad Salaria Tehseen Mazhar + 4 lainnya
2024
Energy efficiency and safety are two essential factors that play a significant role in operating a wireless sensor network. However, it is claimed that these two factors are naturally conflicting. The level of electrical consumption required by a security system is directly proportional to its degree of complexity. Wireless sensor networks require additional security measures above the capabilities of conventional network security protocols, such as encryption and key management. The potential application of machine learning techniques to address network security concerns is frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them to understand their environment and respond. During the training phase, machine-learning systems may face challenges due to the large amount of data required and the complex nature of the training procedure. The main objective of the article is to know about different machine learning algorithms that are used to solve the security issues of wireless sensor networks. This study also focuses on the use of wireless sensor networks in different fields. Furthermore, this study also focuses on different Machine learning algorithms that are used to secure wireless sensor networks. Moreover, this study also addresses issues of adapting machine learning algorithms to accommodate the sensors’ functionalities in the network configuration. Furthermore, this article also focuses on open issues in this field that must be solved.
Sarumathi Murali A. Jamalipour
15 Juni 2022
The Internet of Things (IoT) is an emerging technology that has earned a lot of research attention and technical revolution in recent years. Significantly, IoT connects and integrates billions of devices and communication networks around the world for several real-time IoT applications. On the other hand, cybersecurity attacks on the IoT are growing at an alarming rate since these devices are vulnerable because of their limited battery life, global connectivity, resource-constrained nature, and mobility. When attacks on IoT networks go undetected within a speculated period, such security attacks may prompt severe threats and disruptive behavior inside the network and make the network unavailable to the end user. Hence, it is quintessential to design an intelligent and robust security approach that promptly detects potential attack surfaces in a dynamic IoT network. This article investigates a comprehensive survey of machine learning, deep learning, and reinforcement learning-based intelligent intrusion detection techniques for securing IoT. Also, this article thoroughly illustrates the implementation of various categories of security threats in IoT with a neat diagram. Significantly, we classify the threats into two broad categories: 1) wireless sensor networks (WSNs) inherited security attacks and 2) routing protocol for low power and lossy networks (RPL) specific security attacks in IoT. Finally, we present potential research opportunities and challenges in intelligent intrusion detection approaches in future IoT security.
S. Ameen S. H. Haji
2 Juni 2021
The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.
Aashma Uprety D. Rawat
14 Februari 2021
The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and reinforcement learning (RL) is gaining more popularity for securing IoT systems. RL, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this article, we present an comprehensive survey of different types of cyberattacks against different IoT systems and then we present RL and deep RL-based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the RL for securing CPS systems (i.e., IoT with feedback and control), such as smart grid and smart transportation system. The recent important attacks and countermeasures using RL in IoT are also summarized in the form of tables. With this article, readers can have a more thorough understanding of IoT security attacks and countermeasures using RL, as well as research trends in this area.
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ESEERP: Enhanced Smart Energy Efficient Routing Protocol for Internet of Things in Wireless Sensor Nodes
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1 Agustus 2022
Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet delivery ratio and network longevity, the proposed methodology is mathematically studied, and the results have been compared to identical current approaches such as a Genetic algorithm (GA), Ant Lion optimization (ALO) and Particle Swarm Optimization (PSO). The simulation shows that in the proposed approach for the longevity of the network, there are 3500 rounds; energy utilization achieves a maximum of 0.5 Joules; bandwidth transmits the data at the rate of 0.52 MBPS; the packet delivery ratio (PDR) is at the rate of 96% for 500 nodes, respectively.